Hospital-Based Medicine

A deep learning classification framework for research methods of marine protected area management.

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient approaches for theoretically based and data-driven method integration. However, due to the rapid development and diversification of research methods, it is growingly difficult to locate new methods in methodological dimensions and integrate them to the utmost utility. This study proposes a deep lea...

Multi-task heterogeneous graph learning on electronic health records.

Learning electronic health records (EHRs) has received emerging attention because of its capability to facilitate accurate medical diagnosis. Since the EHRs contain enriched information specifying complex interactions between entities, modeling EHRs with graphs is shown to be effective in practice. The EHRs, however, present a great degree of heterogeneity, sparsity, and complexity, which hamper t...

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes extended waiting times and increased patient dissatisfa...

Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

AIMS: Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patients were significantly different from those of hear...

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Diseas...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associat...

Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.

Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsis outcomes early is vital. Despite machine learnin...

Identification of medication-related fall risk in adults and older adults admitted to hospital: A machine learning approach.

The study aimed to develop and validate, through machine learning, a fall risk prediction model related to prescribed medications specific to adults a...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiatric treatment and the months post-discharge is a h...

A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.

The issue of left against medical advice (LAMA) patients is common in today's emergency departments (EDs). This issue represents a medico-legal risk a...

How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.

Applications of large language models (LLMs) in the healthcare field have shown promising results in processing and summarizing multidisciplinary info...

Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning.

UNLABELLED: Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. In patients with a return of spontaneous circulatio...

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that...

Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A proof-of-concept.

INTRODUCTION: Delays in discharging patients from Acute Medical Units hamper patient flows throughout the hospital. The decision to discharge a patien...

Admission blood tests predicting survival of SARS-CoV-2 infected patients: a practical implementation of graph convolution network in imbalance dataset.

BACKGROUND: Predicting an individual's risk of death from COVID-19 is essential for planning and optimising resources. However, since the real-world m...

Assessing the use of the novel tool Claude 3 in comparison to ChatGPT 4.0 as an artificial intelligence tool in the diagnosis and therapy of primary head and neck cancer cases.

Head and neck squamous cell carcinoma (HNSCC) is a complex malignancy that requires a multidisciplinary tumor board approach for individual treatment ...

Navigation benchmarking for autonomous mobile robots in hospital environment.

The widespread adoption of robotic technologies in healthcare has opened up new perspectives for enhancing accuracy, effectiveness and quality of medi...

Development and validation of prediction models for nosocomial infection and prognosis in hospitalized patients with cirrhosis.

BACKGROUND: Nosocomial infections (NIs) frequently occur and adversely impact prognosis for hospitalized patients with cirrhosis. This study aims to d...

Exploring the Potential of a Smart Ring to Predict Postoperative Pain Outcomes in Orthopedic Surgery Patients.

Poor pain alleviation remains a problem following orthopedic surgery, leading to prolonged recovery time, increased morbidity, and prolonged opioid us...

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine th...